Machine Learning in Manufacturing
Discover how machine learning can transform your manufacturing business and add to your business value.
Those days are long gone when artificial intelligence and machine learning sounded like sci-fi movies to us. Today we are going through the Fourth Industrial Revolution or simply Industry 4.0, whose key drivers are large volumes of data and rapid technological advances. This couldn’t be more appropriate for the manufacturing sector, which has got closer to its timeless goal of producing more quality goods at lower expenses thanks to ML. So, what is this hype around machine learning and manufacturing, and how can your company unlock unprecedented growth if it introduces ML to its operations?
Top challenges in manufacturing
In 2021, manufacturers face at least two major problems that stop them from achieving long-term success and that ML technology can help them solve.
First, have you heard about the skilled talent shortage in the industry today? While the demand for manufactured goods continues to grow, companies find it difficult to hire enough skilled laborers to meet this demand. If not solved, this problem can result in $2.5 trillion losses by the end of this decade. Fortunately, machine learning can help you bridge this gap by optimizing production so machines complete more complex tasks.
Secondly, the COVID-19 pandemic has affected manufacturing like any other sector, bringing in uncertainty, supply chain disruptions, and affecting contribution margins. For instance, it has complicated inventory and supply chain management, with manufacturers keeping too little inventory and risking losing profits and ruining customer experience. Holding too much inventory is risky in times of coronavirus too — you spend extra on storage and may not sell everything.
You can minimize unpredictability with machine learning, though. This technology allows you to build more agile manufacturing processes and enable multi-target tracking, for example, as in the case of supply chain management. Moreover, there are lots of other ways to apply machine learning in business, which we’ll discuss below.
Use cases of ML in manufacturing
As a game-changing technology, machine learning brings next-gen optimization in manufacturing and, thus, has different use cases. Let’s consider the most critical ones:
1. Predictive maintenance
By using ML algorithms, a company checks the condition of equipment and predicts its future failure. This way, your staff can respond in advance, preventing downtime and extending equipment life.
2. Digital twins
ML takes a great part in building digital twins, so-called digital copies of a product, its component, or a process. By using digital twins, a manufacturer can then conduct real-time diagnostics, find anomalies, and optimize its processes. WSJ informs about Unilever PLC, a consumer-good giant, that has built virtual versions of its factories to track and improve physical conditions such as temperature or motor speed.
3. Energy consumption forecasting
The combined effort of IoT devices and machine learning allows you to gather enough historical data about lighting, humidity, activity levels of the facility, and so on and then use these data to predict and save up on energy consumption.
4. Generative design
Together with AI, ML enables creating as many design solutions as needed, without wasting company resources. What’s left for you as a manufacturer is to select the most suitable design option and put it in production. This technology is growing especially popular in the automotive industry. Here’s how Volkswagen Group incorporates generative design in its operations:
5. Predictive quality and yield
Another advanced use of ML and AI algorithms includes analyzing every factor that impacts your production output. This way, your company can discover the root causes of its losses such as those related to yield, quality, or waste, mitigate the risks, and add up to its competitive advantage.
Benefits of ML in manufacturing
As you see, the adoption of AI and ML, in particular, can bring lots of value to your manufacturing business. Here are specific wins you can get:
- Reduced cycle time and scrap and better resource utilization, for example, by predicting demand better and improving production schedules
- Continuous quality improvement such as by using ML to find and remove defective products faster and avoid performance errors in machinery
- Increased operational efficiency through tracking the entire manufacturing cycle, seeking, and removing bottlenecks
- Reduced maintenance costs and improved reliability such as by extending life of equipment and machinery
- Enhanced safety on the factory floor
Wrap up
Industry 4.0 is already here, bringing disrupting technologies into the manufacturing industry. As a subset of AI, machine learning enables businesses to reach unmatched performance, without compromising quality. So, isn’t it the best time to implement ML and revolutionize the way your business operates? And in case you need any assistance, our team of experienced developers is ready to support you throughout the whole AI/ML adoption journey.
If you’re interested in adding value to your manufacturing business via completing an ML project, it’s high time to get started. ML experts from Intelliarts AI’s team are here to help you. To learn more and overcome your competitors, feel free to reach out.
